Physically Based Rendering, Fourth Edition: From Theory to Implementation (4版)

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【簡介】 A comprehensive update of the leading-edge computer graphics textbook that sets the standard for physically-based rendering in the industry and the field, with new material on GPU ray tracing.Photorealistic computer graphics are ubiquitous in today’s world, widely used in movies and video games as well as product design and architecture. Physically-based approaches to rendering, where an accurate modeling of the physics of light scattering is at the heart of image synthesis, offer both visual realism and predictability. Now in a comprehensively updated new edition, this best-selling computer graphics textbook sets the standard for physically-based rendering in the industry and the field. Physically Based Rendering describes both the mathematical theory behind a modern photorealistic rendering system as well as its practical implementation. A method known as literate programming combines human-readable documentation and source code into a single reference that is specifically designed to aid comprehension. The book’s leading-edge algorithms, software, and ideas--including new material on GPU ray tracing--equip the reader to design and employ a full-featured rendering system capable of creating stunning imagery. This essential text represents the future of real-time graphics. Detailed and rigorous but accessible approach guides readers all the way from theory to practical software implementationFourth edition features new chapter on GPU ray tracing essential for game developersThe premier reference for professionals learning about and working in the fieldWon its authors a 2014 Academy Award for Scientific and Technical Achievement Includes a companion site complete with source code

Computing and Technology Ethics: Engaging Through Science Fiction (1版)

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【簡介】 A new approach to teaching computing and technology ethics using science fiction stories.Should autonomous weapons be legal? Will we be cared for by robots in our old age? Does the efficiency of online banking outweigh the risk of theft? From communication to travel to medical care, computing technologies have transformed our daily lives, for better and for worse. But how do we know when a new development comes at too high a cost? Using science fiction stories as case studies of ethical ambiguity, this engaging textbook offers a comprehensive introduction to ethical theory and its application to contemporary developments in technology and computer science. Computing and Technology Ethics: Engaging through Science Fiction first introduces the major ethical frameworks: deontology, utilitarianism, virtue ethics, communitarianism, and the modern responses of responsibility ethics, feminist ethics, and capability ethics. It then applies these frameworks to many of the modern issues arising in technology ethics including privacy, computing, and artificial intelligence. A corresponding anthology of science fiction brings these quandaries to life and challenges students to ask ethical questions of themselves and their work. Uses science fiction case studies to make ethics education engaging and fun Trains students to recognize, evaluate, and respond to ethical problems as they ariseFeatures anthology of short stories from internationally acclaimed writers including Ken Liu, Elizabeth Bear, Paolo Bacigalupi, and T. C. Boyle to animate ethical challenges in computing technology Written by interdisciplinary author team of computer scientists and ethical theoristsIncludes a robust suite of instructor resources, such as pedagogy guides, story frames, and reflection questions

Multi-Agent Reinforcement Learning: Foundations and Modern Approaches (1版)

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【簡介】 The first comprehensive introduction to Multi-Agent Reinforcement Learning (MARL), covering MARL’s models, solution concepts, algorithmic ideas, technical challenges, and modern approaches.Multi-Agent Reinforcement Learning (MARL), an area of machine learning in which a collective of agents learn to optimally interact in a shared environment, boasts a growing array of applications in modern life, from autonomous driving and multi-robot factories to automated trading and energy network management. This text provides a lucid and rigorous introduction to the models, solution concepts, algorithmic ideas, technical challenges, and modern approaches in MARL. The book first introduces the field’s foundations, including basics of reinforcement learning theory and algorithms, interactive game models, different solution concepts for games, and the algorithmic ideas underpinning MARL research. It then details contemporary MARL algorithms which leverage deep learning techniques, covering ideas such as centralized training with decentralized execution, value decomposition, parameter sharing, and self-play. The book comes with its own MARL codebase written in Python, containing implementations of MARL algorithms that are self-contained and easy to read. Technical content is explained in easy-to-understand language and illustrated with extensive examples, illuminating MARL for newcomers while offering high-level insights for more advanced readers. First textbook to introduce the foundations and applications of MARL, written by experts in the fieldIntegrates reinforcement learning, deep learning, and game theoryPractical focus covers considerations for running experiments and describes environments for testing MARL algorithmsExplains complex concepts in clear and simple languageClassroom-tested, accessible approach suitable for graduate students and professionals across computer science, artificial intelligence, and robotics Resources include code and slides

AI & I: An Intellectual History of Artificial Intelligence (1版)

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【簡介】 A concise and illuminating history of the field of artificial intelligence from one of its earliest and most respected pioneers.AI & I is an intellectual history of the field of artificial intelligence from the perspective of one of its first practitioners, Eugene Charniak. Charniak entered the field in 1967, roughly 12 years after AI’s founding, and was involved in many of AI’s formative milestones. In this book, he traces the trajectory of breakthroughs and disappointments of the discipline up to the current day, clearly and engagingly demystifying this oft revered and misunderstood technology. His argument is controversial but well supported: that classical AI has been almost uniformly unsuccessful and that the modern deep learning approach should be viewed as the foundation for all the exciting developments that are to come. Written for the scientifically educated layperson, this book chronicles the history of the field of AI, starting with its origin in 1956, as a topic for a small academic workshop held at Dartmouth University. From there, the author covers reasoning and knowledge representation, reasoning under uncertainty, chess, computer vision, speech recognition, language acquisition, deep learning, and learning writ large. Ultimately, Charniak takes issue with the controversy of AI--the fear that its invention means the end of jobs, creativity, and potentially even humans as a species--and explains why such concerns are unfounded. Instead, he believes that we should embrace the technology and all its potential to benefit society.

Veridical Data Science: The Practice of Responsible Data Analysis and Decision Making (1版)

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【簡介】 Using real-world data case studies, this innovative and accessible textbook introduces an actionable framework for conducting trustworthy data science.Most textbooks present data science as a linear analytic process involving a set of statistical and computational techniques without accounting for the challenges intrinsic to real-world applications. Veridical Data Science, by contrast, embraces the reality that most projects begin with an ambiguous domain question and messy data; it acknowledges that datasets are mere approximations of reality while analyses are mental constructs. Bin Yu and Rebecca Barter employ the innovative Predictability, Computability, and Stability (PCS) framework to assess the trustworthiness and relevance of data-driven results relative to three sources of uncertainty that arise throughout the data science life cycle: the human decisions and judgment calls made during data collection, cleaning, and modeling. By providing real-world data case studies, intuitive explanations of common statistical and machine learning techniques, and supplementary R and Python code, Veridical Data Science offers a clear and actionable guide for conducting responsible data science. Requiring little background knowledge, this lucid, self-contained textbook provides a solid foundation and principled framework for future study of advanced methods in machine learning, statistics, and data science. Presents the Predictability, Computability, and Stability (PCS) methodology for producing trustworthy data-driven resultsTeaches how a data science project should be conducted from beginning to end, including extensive discussion of the data scientist’s decision-making processCultivates critical thinking throughout the entire data science life cycleProvides practical examples and illuminating case studies of real-world data analysis problems with associated code, exercises, and solutionsSuitable for advanced undergraduate and graduate students, domain scientists, and practitioners